Graph augmentation and encoding. Given a list of \(n\)  graph examples,
we first apply stochastic data augmentation to transform any given graph example
into two correlated views randomly (\(2n\)  views in total).
We consider various augmentation techniques, including node/edge perturbation [1]},
subgraph sampling [1]},
diffusion [3]}, and adaptive methods [4]}.
The selection of graph augmentation techniques depends on the actual data domain [1]}.
To pre-train an expressive GNN encoder,
we consider whether a pair of graph views denoted by \(\tilde{G}_1\) 
and \(\tilde{G}_2\)  are matched or not based on their graph representations.
Specially, we first apply the GNN encoder to obtain node representations in the two graph views.
Let \(\mathbf {h}^{(1)}_s\)  and \(\mathbf {h}^{(2)}_t\)  denote the representations of node \(s\)  from \(\tilde{G}_1\)  and node \(t\)  from \(\tilde{G}_2\) , respectively.
